Muhammad Imran Ahmad
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Preferred name
Muhammad Imran Ahmad
Official Name
Muhammad Imran, Ahmad
Alternative Name
Ahmad, Muhammad Imran
Ahmad, M. I.
Imran Ahmad, Muhammad
Ahmad, Muhamad Imran
Main Affiliation
Scopus Author ID
57214845678
Researcher ID
GBE-1471-2022

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  • Publication
    AI Assisted and IOT Based Fertilizer Mixing System
    (Universiti Malaysia Perlis, 2024-06-03)
    Wan Mohd Faizal Wan Nik
    ;
    Shahrul Fazly Man@Sulaiman
    ;
    Muhammad Imran Ahmad
    ;
    Shafie Omar
    ;
    Tan Shie Chow
    ;
    Mohd Nazri Abu Bakar
    ;
    Fadhilnor Abdullah
    ;
    Muhammad Khamil Akbar
    Agriculture techniques, particularly fertilizer mixing, have significant impacts on crop productivity. Introducing IoT technology to agriculture can enhance productivity, and machine learning offers a mechanism to gain insights from data, making agricultural practices more efficient. This research aims to design an AI-assisted and IoT-based fertilizer mixing system for greenhouses. This system utilizes sensor data and AI algorithms, specifically the Support Vector Machine (SVM), to optimize fertilizer application. Results from the SVM classifier showed a 100% accuracy rate for temperature and humidity, 65% accuracy for phosphorus, 86% for nitrogen, and 100% for potassium. These findings demonstrate the potential of the proposed system to improve fertilizer efficiency while reducing labor and resource waste.
  • Publication
    IoT Enabled Mushroom Farm Automation with Machine Learning
    (Universiti Malaysia Perlis, 2024-06-03)
    Shafie Omar
    ;
    Wan Mohd Faizal Wan Nik
    ;
    Muhammad Imran Ahmad
    ;
    Tan Shie Chow
    ;
    Mohd Nazri Abu Bakar
    ;
    Shahrul Fazly Man@Sulaiman
    ;
    Fadhilnor Abdullah
    ;
    Vikneshwara Ram Suppiah
    Mushroom farming has gained prominence due to its significant contribution to the global market. One major challenge for mushroom cultivation is maintaining optimal environmental conditions, specifically temperature and humidity. Traditional farming methods, prevalent in many parts of the world, lack precise control over these parameters, often leading to poor yield. This paper presents an innovative approach combining the Internet of Things (IoT) and Machine Learning (ML) for mushroom farm automation. The proposed system employs the ESP8266 microcontroller with specific agricultural sensors for smart monitoring. To regulate the farm's environmental conditions, ML algorithms predict mushroom farm weather states: mild, normal, and hot. The ensemble ML model, comprising five classifiers – Decision Tree, Logistic Regression, K-nearest neighbor, Support Vector Machine, and Random Forest – delivers a commendable accuracy of 100% when combining predictions, surpassing the performance of individual classifiers. This integrated IoT and ML approach promises to revolutionize real-time automation and cultivation practices in the mushroom industry.
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